Summary of Towards Goal-oriented Prompt Engineering For Large Language Models: a Survey, by Haochen Li et al.
Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey
by Haochen Li, Jonathan Leung, Zhiqi Shen
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper examines the current state of Large Language Models (LLMs) and their performance in various tasks. The authors highlight the limitations of traditional prompt engineering methods that rely on anthropomorphic assumptions about how LLMs think. Instead, they propose a goal-oriented approach that guides LLMs to follow human logical thinking, resulting in significant performance improvements. The paper also introduces a novel taxonomy for categorizing goal-oriented prompting methods and demonstrates their broad applicability across various fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can do many tasks, like understand language. But making them work well is tricky! People usually try to “teach” the models by giving them hints or questions to answer. This paper looks at how people make these hints and shows that it’s not working very well. The authors have a new way of making hints that makes the models do better, and they explain why this is important for many areas, like science and technology. |
Keywords
» Artificial intelligence » Prompt » Prompting
